C04-1099 classifler with the vector-space classifler . Unlike ( Larkey and Croft ,
C04-1099 combines the regular expression classifler with the vector-space classifler
C04-1033 algorithm to learn a decision tree classifler as in the baseline approach .
C04-1033 training instances are ready , a classifler is learned by C5 .0 algorithm
C04-1099 regarding each MeSH term . The classifler is tuned by using English abstracts
C04-1099 component . Each of the basic classiflers implement known approaches to
C04-1033 that our approach aims to learn a classifler which would select the most preferred
C04-1099 number of relevant terms is 15193 . classiflers . Regular expressions and MeSH
C04-1099 more than 5 tokens . The second classifler is based on a vector space engine5
C04-1107 can be estimated by some binary classiflers . For instance , we could estimate
C04-1099 Croft , 1996 ) we do not merge our classiflers by linear combination , because
C04-1033 clusters . In our approach , a classifler is trained on the instances formed
C04-1099 with the regular expression-based classifler . 3 Methods We flrst present
C04-1099 parameters ( lnc.atn ) for the basic VS classifler does not provide the optimal
C04-1099 matching window . Vector space classifler . The vector space module is
C04-1033 prob - lem . Speciflcally , a classifler is learned and then used to determine
C04-1099 such as syndrome and disease ) . Classiflers ' fusion . The hybrid system
C04-1107 for a group mi : We use one Svmc classifler to identify the group to which
C04-1099 parameters of the vector space classifler , and the best combination of
C04-1099 respective performance of each basic classiflers , table 1 shows that the RegEx
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